Already well established in food retail, in-store activity management is built around a few key principles: ensuring the store is well presented, optimizing checkout wait times, and allocating both time and the right people to deliver all the services linked to digitalization. In specialized retail, however, the critical and differentiating challenge lies in sales advice — and in the store’s ability to assign staff to the right place at the right time to properly support customers.
The real challenge is in managing a high-stakes balancing act between the time spent advising customers and the time dedicated to purely operational tasks — all within a legal framework that doesn’t always allow flexibility in scheduling. The task can be rather chaotic, given the sharp fluctuations in activity: a workload assessed at a given moment can shift rapidly based on many factors.
So how can you evaluate the daily workload in order to build an effective work schedule? What level of granularity should you choose (individual employee, team…) to pragmatically manage store operations?
The answers to these questions are in this article!
I) How can you assess the workload involved in sales assistance?
There is only one method to assess this workload.
Identify product categories and calculate sales time
First, identify the product categories that require sales assistance. Then, determine the level of advice needed (1, 2, or 3) and the associated consultation time for each level. On-site observation and time measurement are essential. The nature of the business sector and the products must be taken into account, as they influence the weighting between different levels of advice that may be needed. It is therefore crucial to fully understand the customer journey and shopping behavior in the store, depending on the type of product, in order to identify the various sales processes.
Example: a large specialized sporting goods store
Some purchases require almost systematic advice (known as level 1), such as buying a tennis racket or an adult bike. Others, like mountain hiking shoes or a trekking jacket, may require less intensive advice (level 2), aimed at reassuring the customer and finalizing the sale. Products like cycling apparel may require only occasional advice (level 3), such as locating items in the store, helping with sizing, or simply reassuring the customer about their choice.
Example: a pharmacy
The consultation time varies depending on the nature of the products:
Prescription medication requires the presence of a staff member who retrieves the medicine, reviews the dosage, and provides explanations for proper adherence to the treatment — usually averaging 6 to 8 minutes.
Over-the-counter health and beauty products (parapharmacy) may require varying levels of advice depending on the customer’s familiarity with the product — around 2 to 4 minutes on average.
Self-service items require little to no assistance — around 1 to 2 minutes on average.
These levels of advice, and the time allocated to each, help determine the workload.
To do so effectively, you must be able to accurately forecast the number of sales per product category.
Forecast the number of sales per product category
The goal is to forecast sales levels for these different products based on the store’s sales history and supported by a predictive algorithm. This essentially means asking: ‘What types of products are sold, and at what times of the day?
Let’s take the example of the ‘tennis rackets’ category in a sporting goods store:
The objective is to determine how many tennis rackets the store expects to sell next Friday between 11:00 AM and 12:00 PM.
- To calculate this, the store typically relies on historical checkout data — detailed sales records — ideally covering a period of at least 1 to 2 years.
From there, it’s important to analyze the factors that may have influenced sales activity — promotions, special events or competitions, weather, etc. — in order to understand their impact on sales trends. - Once this analysis is completed, the dataset is enriched with daily-updated information: changes in sales by product category and integration of external data. Thanks to AI, predictive algorithms improve the accuracy and reliability of the forecasts.
- Finally, the projected quantities of products sold are matched with the consultation time data to calculate the total workload.
- Based on the responsibilities involved, it becomes possible to estimate, by the day or even by the hour, the staff required to cover this workload.
Returning to our example: if the estimated consultation time between 11:00 and 12:00 is 3 hours, then 3 staff members must be assigned to the tennis rackets section during that time.
Note: The workload must also be calculated for all other operational tasks (shelf restocking, inventory management, etc.), usually based on actual flow data.
Once all these workloads have been calculated, how can they be used operationally to effectively manage the activity?
II) What type of scheduling should you use: should activity be managed by individual employee, by function, or by team?
Several factors must be considered when choosing the type of schedule needed to manage store activity:
Store size
Organization of work: whether staff are multi-skilled or not
The level of operational precision required to ensure high-quality sales assistance or store standards
It is the combination of these factors that determines the type of scheduling to implement:
Managing overall workload:
This approach is ideal for small-format stores with a flexible, multi-skilled team. This type of schedule allows you to determine the volume of work required at any given time of the day. The key challenge is ensuring enough resources are available during each time slot.
For example, a pharmacist might ask:
“Do I have enough staff to handle the activity between 9 and 10 a.m., knowing that the workload consists of 3 hours of sales assistance/cash register duties, 2 hours of restocking, and 1 hour of order preparation?”
This method helps identify periods of potential over- or under-staffing. As a result, you can reprioritize certain tasks or adjust schedules if these imbalances occur frequently. It allows you to better utilize your teams during downtime.
Managing by individual employee:
It is also possible to monitor each person’s schedule based on the store’s operational needs or in cases where specific expertise is required. This type of scheduling provides visibility into the workload assigned to each person, helping identify free time or potential overload.
This enables workload balancing: either by redistributing tasks when one individual is overwhelmed or by assigning additional tasks when an employee has free time—always taking their skill set into account. It also helps detect if a recurring need exists at a specific time, potentially leading to schedule adjustments for that employee.
Managing by function:
This scheduling type applies to groups of employees who are interchangeable within the same function, but not across functions, since the workload corresponds to a specific type of task.
It allows for visibility over a collective workload schedule for a function, while still maintaining individual schedules in parallel.
Managing store operations is complex. It requires several areas of expertise, such as task time tracking, deep knowledge of store processes, the ability to factor in different levels of sales assistance, and a good understanding of AI.
Conclusion
The TimeSkipper activity management platform brings together all these skills in a single, ready-to-use tool.
Designed for retail leadership teams, it enables fast and easy adoption. The platform models store operations in a way that clearly identifies the nature of the workload — based on product categories — and the time required for each task. TimeSkipper also organizes in-store time tracking campaigns, validated with the teams.
Its offer includes a forecasting module based on algorithmic and Artificial Intelligence technologies, allowing you to quickly identify the types of products being sold and calculate the related sales assistance workload.
Only by relying on this tool can you find the right balance between providing excellent customer service and handling operational tasks — all while ensuring store profitability.
